{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from models.ctc_loss import CTCLoss\n",
    "from models.accuracy import WordAccuracy\n",
    "from models.decode import Decoder\n",
    "from models.config import BATCH_SIZE, BUFFER_SIZE, WORK_PATH,TEST_PATH, is_save_model, TABLE_PATH\n",
    "from models.data_prepare import load_and_preprocess_image, decode_label, get_image_path,load_and_preprocess_image_pridict, load_and_preprocess_image_standard\n",
    "import numpy as np\n",
    "import random\n",
    "import json\n",
    "import time\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 构造数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#测试数据集\n",
    "test_all_image_paths = [TEST_PATH + img for img in sorted(os.listdir(TEST_PATH))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_images_num = len(test_all_image_paths)\n",
    "test_steps_per_epoch = test_images_num\n",
    "test_ds = tf.data.Dataset.from_tensor_slices(test_all_image_paths)\n",
    "test_ds = test_ds.map(load_and_preprocess_image_pridict, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n",
    "test_ds = test_ds.repeat()\n",
    "test_ds = test_ds.batch(len(test_all_image_paths))\n",
    "test_ds = test_ds.apply(tf.data.experimental.ignore_errors())\n",
    "test_ds = test_ds.prefetch(tf.data.experimental.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model(WORK_PATH + 'output/crnn_5.h5', compile=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss=CTCLoss(), metrics=[WordAccuracy()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = next(iter(test_ds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = model.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(TABLE_PATH, 'r',encoding='gbk') as f:\n",
    "    inv_table = [char.strip() for char in f]+[' ']*2\n",
    "    \n",
    "decoder = Decoder(inv_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2304x5760 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2304x5760 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2304x5760 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2304x5760 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2304x5760 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_pred = decoder.decode(result, method='greedy')\n",
    "\n",
    "for i,sentense in enumerate(y_pred):\n",
    "    print('---------------------------------------')\n",
    "    plt.figure(figsize=(32,80))\n",
    "    plt.subplot(len(y_pred),1,i+1)\n",
    "    plt.imshow(load_and_preprocess_image_standard(test_all_image_paths[i]))\n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.xlabel('识别结果： '+sentense)\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 保存模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "if is_save_model:\n",
    "    tf.keras.models.save_model(\n",
    "                                model,\n",
    "                                export_path,\n",
    "                                overwrite=True,\n",
    "                                include_optimizer=True,\n",
    "                                save_format=None,\n",
    "                                signatures=None,\n",
    "                                options=None\n",
    "                              )\n",
    "    print('模型保存成功。')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 查看已保存的模型信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!saved_model_cli show --dir {export_path} --all"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
